Flooding Capacity in Packed Towers:  Database, Correlations, and Analysis

Piché, Simon; Larachi, Faı̈çal; Grandjean, Bernard P. A.

doi:10.1021/ie000486s

Cited by 40 publications

(37 citation statements)

References 26 publications

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“…Very recently, Piche et al (2001a) presented a flooding correlation based on the combination of artificial neural network modelling and dimensional analysis. They compared the accuracy of the neural correlation and 14 existing flooding correlations against a wide collection of experimental data from the literature.…”

Section: Floodingmentioning

confidence: 99%

“…More recently, Szekely and Mendrykowski's (1972) flooding data for mercury-nitrogen and mercury-helium systems were among the wide collection of experimental data tested by Piche et al (2001a) using their flooding correlation derived from artificial neural network modelling. The neural correlation reportedly gave average absolute relative errors of 14.3% and 41.1 % for the two systems respectively.…”

Section: Flooding In Non-wetting Systemsmentioning

confidence: 99%

“…The neural correlation's development was based on preexisting flooding data mainly obtained from conventional wettable systems using aqueous and organic liquids. Hence although it gives remarkable accuracy in predicting the flooding capacities of conventional systems compared to other existing flooding correlations (Piche et al .. 2001a), the neural correlation's extension to non-wettable systems is still limited as demonstrated in Table 6.12.…”

Section: I:l Pressure Drop At Low Air Velocitiesmentioning

confidence: 99%

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A Non-Wetting Packed Bed Gas Scrubber

Chang

Swithenbank

Sharifi

2007

Process Safety and Environmental Protection

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SummaryPresent integrated gasification combined cycle (IGCC) systems demonstrate high system efficiency and impressive environmental performance, giving them an edge over conventional pulverised fuel power stations. A key area in the development of IGCCs is hot fuel gas clean-up (HGCU). Fuel gas cleaning at elevated temperatures reduces thermal efficiency losses associated with gas quenching in conventional cold gas cleaning methods. Current hot gas desulphurisation techniques focus on the use of regenerable metal oxide sorbents, however the long-term sorbent performance issues have yet to be fully addressed. A fresh and radical approach may provide the key to overcoming the inherent limitations associated with metal oxide sorbents. A molten tin irrigated packed bed scrubber adopted in this research project is one such innovative way forward in HGCU. The hot scrubber offers the prospect of a multicomponent clean-up device.High-temperature sulphur removal takes place via absorption of H 2 S (and COS) into molten tin whilst discrete molten tin droplets and rivulets on the packing surface act as solid particulate collectors.The primary aim of this research project was to investigate the workings of a small-scale roomtemperature packed bed scrubber operating under non-wetting flow conditions analogous to the molten tin irrigated scrubber. Water irrigation of low surface energy packings simulated the nonwetting flow of liquid metals. The air-water analogue of the liquid metal scrubber provided the platform for hydrodynamics (flow visualisation, flooding and liquid holdup), particulate removal and mass transfer studies under non-wetting flow conditions. The performance of a small air lift for water circulation through the column was also investigated. These cold studies offered insight into the operation and performance of the liquid metal hot scrubber.Prior to the cold gas scrubber studies, preliminary small-scale gasification tests on petroleum coke samples were performed to investigate the effect of molten tin on H 2 S in the product fuel gas. The tests provided actual experimental evidence of the possibility of sulphur removal by molten tin in a gasification environment.It was shown that the maximum possible size of a liquid droplet hanging from a non-wetting spherical solid surface could be predicted from the liquid surface tension and density based on force balance. The mobility of static holdup in a non-wettable packed bed has been demonstrated, this being due to the tendency for the liquid to form discrete droplets rather than spreading films. Existing flooding and liquid holdup correlations that hold for conventional wettable packed beds were shown to be inadequate where non-wetting systems were concerned. Summary hence alternative methods applicable to the latter were sought. The introduction of a non-wetting tendency factor based on the ratio of the solid critical surface tension to the liquid surface tension, enabled the flooding capacities of non-wetting systems including those of this study to be predict...

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Section: Floodingmentioning

confidence: 99%

Section: Flooding In Non-wetting Systemsmentioning

confidence: 99%

Section: I:l Pressure Drop At Low Air Velocitiesmentioning

confidence: 99%

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A Non-Wetting Packed Bed Gas Scrubber

Chang

Swithenbank

Sharifi

2007

Process Safety and Environmental Protection

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“…However, most of them require the specific empirical constants related to the packings under consideration [3]. Unfortunately, these constants are difficult to obtain for every packing element, which clearly illustrate a lack of generalization [4]. Also, with the rapid development of packings, there are more and more packing constants need to determine using experiments, which may be a time-and-energy-consuming task.…”

Section: Introductionmentioning

confidence: 99%

“…Based on the advantages of soft sensor modeling methods with increasing applications in many other fields [5,6], neural networks (NN) have been used to predict the flooding velocity by Piche et al [4]. According to his report, the prediction performance of NN model for the flooding velocity has an improvement than traditional empirical models.…”

Section: Introductionmentioning

confidence: 99%

Prediction of flooding velocity in packed towers using least squares support vector machine

Liu

Yang

et al. 2012

Proceedings of the 10th World Congress on Intelligent Control and Automation

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The flooding velocity is an important but difficult to accurately predict parameter for the packed column design. With the appearance of new packing shapes, traditional empirical models are insufficient to satisfy the requirement of engineering applications. In this paper, a novel approach using least squares-support vector machine (LS-SVM) is proposed to predict the flooding velocity in the randomly dumped packed towers. To evaluate the performance of the LS-SVM model applied to predict the flooding velocity, it is compared with the traditional empirical models and the neural network models. It is found that the LS-SVM model can provide the best performance of all, with an average absolute relative error less than 8 %. The results demonstrate that LS-SVM offers an alternative approach to model and predict the flooding velocity in the randomly dumped packed towers.

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Absorption

Karayiannis

Laso

2018

Kirk-Othmer Encyclopedia of Chemical Technology

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Absorption, or gas absorption, is a unit operation used in the chemical industry to separate gases by washing or scrubbing a gas mixture with a suitable liquid. One or more of the gas components dissolves or is absorbed in the liquid and is thus removed from the mixture. Absorption is currently one of the most important processes in chemical engineering not only for product recovery but also, and perhaps mainly, for pollutant removal. Intense industrial and academic effort is focused on optimizing the process, in terms process efficiency as well as commercial aspects. In this article, the fundamental principles of gas solubility and mass transfer, design and operation of industrial equipment with relevant examples of typical commercial gas absorption processes are discussed, including recent developments in these areas.

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Flooding Capacity in Packed Towers: Database, Correlations, and Analysis

Cited by 40 publications

References 26 publications

A Non-Wetting Packed Bed Gas Scrubber

A Non-Wetting Packed Bed Gas Scrubber

Prediction of flooding velocity in packed towers using least squares support vector machine

Absorption

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